Overview

Dataset statistics

Number of variables14
Number of observations5971
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory699.7 KiB
Average record size in memory120.0 B

Variable types

Numeric14

Warnings

purchases is highly correlated with quantity_p and 1 other fieldsHigh correlation
devolutions is highly correlated with quantity_p and 3 other fieldsHigh correlation
recency_p is highly correlated with avg_recency_daysHigh correlation
recency_d is highly correlated with invoices_dHigh correlation
quantity_p is highly correlated with purchases and 4 other fieldsHigh correlation
quantity_d is highly correlated with devolutions and 3 other fieldsHigh correlation
invoices_p is highly correlated with purchases and 1 other fieldsHigh correlation
invoices_d is highly correlated with recency_d and 1 other fieldsHigh correlation
avg_ticket is highly correlated with devolutions and 3 other fieldsHigh correlation
avg_recency_days is highly correlated with recency_pHigh correlation
avg_basket_size is highly correlated with devolutions and 3 other fieldsHigh correlation
purchases is highly correlated with quantity_p and 3 other fieldsHigh correlation
devolutions is highly correlated with recency_d and 2 other fieldsHigh correlation
recency_p is highly correlated with invoices_p and 1 other fieldsHigh correlation
recency_d is highly correlated with devolutions and 2 other fieldsHigh correlation
quantity_p is highly correlated with purchases and 2 other fieldsHigh correlation
quantity_d is highly correlated with devolutions and 2 other fieldsHigh correlation
invoices_p is highly correlated with purchases and 4 other fieldsHigh correlation
invoices_d is highly correlated with devolutions and 2 other fieldsHigh correlation
avg_recency_days is highly correlated with recency_p and 1 other fieldsHigh correlation
avg_basket_size is highly correlated with purchases and 2 other fieldsHigh correlation
avg_variety is highly correlated with purchases and 1 other fieldsHigh correlation
purchases_pday is highly correlated with invoices_pHigh correlation
purchases is highly correlated with quantity_p and 2 other fieldsHigh correlation
devolutions is highly correlated with recency_d and 2 other fieldsHigh correlation
recency_p is highly correlated with invoices_p and 1 other fieldsHigh correlation
recency_d is highly correlated with devolutions and 2 other fieldsHigh correlation
quantity_p is highly correlated with purchases and 1 other fieldsHigh correlation
quantity_d is highly correlated with devolutions and 2 other fieldsHigh correlation
invoices_p is highly correlated with purchases and 1 other fieldsHigh correlation
invoices_d is highly correlated with devolutions and 2 other fieldsHigh correlation
avg_recency_days is highly correlated with recency_pHigh correlation
avg_basket_size is highly correlated with purchases and 2 other fieldsHigh correlation
avg_variety is highly correlated with avg_basket_sizeHigh correlation
customer_id is highly correlated with avg_recency_days and 1 other fieldsHigh correlation
invoices_d is highly correlated with purchases and 1 other fieldsHigh correlation
quantity_p is highly correlated with devolutions and 4 other fieldsHigh correlation
recency_d is highly correlated with avg_recency_daysHigh correlation
avg_recency_days is highly correlated with customer_id and 2 other fieldsHigh correlation
devolutions is highly correlated with quantity_p and 4 other fieldsHigh correlation
purchases is highly correlated with invoices_d and 6 other fieldsHigh correlation
avg_basket_size is highly correlated with quantity_p and 4 other fieldsHigh correlation
invoices_p is highly correlated with invoices_d and 1 other fieldsHigh correlation
recency_p is highly correlated with customer_id and 1 other fieldsHigh correlation
quantity_d is highly correlated with quantity_p and 4 other fieldsHigh correlation
avg_ticket is highly correlated with quantity_p and 4 other fieldsHigh correlation
purchases is highly skewed (γ1 = 21.77363976) Skewed
devolutions is highly skewed (γ1 = 50.91642437) Skewed
quantity_p is highly skewed (γ1 = 35.09784254) Skewed
quantity_d is highly skewed (γ1 = 53.23013972) Skewed
avg_ticket is highly skewed (γ1 = 51.96108487) Skewed
avg_basket_size is highly skewed (γ1 = 49.85733829) Skewed
customer_id has unique values Unique
purchases has 215 (3.6%) zeros Zeros
devolutions has 4201 (70.4%) zeros Zeros
quantity_p has 215 (3.6%) zeros Zeros
quantity_d has 4201 (70.4%) zeros Zeros
invoices_p has 215 (3.6%) zeros Zeros
invoices_d has 4201 (70.4%) zeros Zeros
avg_ticket has 215 (3.6%) zeros Zeros
avg_basket_size has 215 (3.6%) zeros Zeros
avg_variety has 215 (3.6%) zeros Zeros
purchases_pday has 215 (3.6%) zeros Zeros

Reproduction

Analysis started2021-06-06 18:15:35.912529
Analysis finished2021-06-06 18:16:02.508990
Duration26.6 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

customer_id
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct5971
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16765.63189
Minimum12346
Maximum22709
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size93.3 KiB
2021-06-06T15:16:02.624185image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum12346
5-th percentile12711
Q114369.5
median16392
Q319241.5
95-th percentile21892.5
Maximum22709
Range10363
Interquartile range (IQR)4872

Descriptive statistics

Standard deviation2882.537033
Coefficient of variation (CV)0.1719313088
Kurtosis-0.9581116481
Mean16765.63189
Median Absolute Deviation (MAD)2180
Skewness0.3706824238
Sum100107588
Variance8309019.745
MonotonicityNot monotonic
2021-06-06T15:16:02.774703image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
163841
 
< 0.1%
156651
 
< 0.1%
156771
 
< 0.1%
217671
 
< 0.1%
177221
 
< 0.1%
156731
 
< 0.1%
133221
 
< 0.1%
177181
 
< 0.1%
156691
 
< 0.1%
214841
 
< 0.1%
Other values (5961)5961
99.8%
ValueCountFrequency (%)
123461
< 0.1%
123471
< 0.1%
123481
< 0.1%
123491
< 0.1%
123501
< 0.1%
123521
< 0.1%
123531
< 0.1%
123541
< 0.1%
123551
< 0.1%
123561
< 0.1%
ValueCountFrequency (%)
227091
< 0.1%
227081
< 0.1%
227071
< 0.1%
227061
< 0.1%
227051
< 0.1%
227041
< 0.1%
227001
< 0.1%
226991
< 0.1%
226961
< 0.1%
226951
< 0.1%

purchases
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct5552
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1785.401859
Minimum0
Maximum280206.02
Zeros215
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size93.3 KiB
2021-06-06T15:16:02.914859image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.75
Q1206.585
median599.97
Q31588.97
95-th percentile5393.625
Maximum280206.02
Range280206.02
Interquartile range (IQR)1382.385

Descriptive statistics

Standard deviation7789.345865
Coefficient of variation (CV)4.362796995
Kurtosis620.2010104
Mean1785.401859
Median Absolute Deviation (MAD)495.15
Skewness21.77363976
Sum10660634.5
Variance60673909
MonotonicityNot monotonic
2021-06-06T15:16:03.063272image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0215
 
3.6%
7.959
 
0.2%
1.258
 
0.1%
2.958
 
0.1%
4.958
 
0.1%
12.757
 
0.1%
1.657
 
0.1%
3.757
 
0.1%
7.56
 
0.1%
4.256
 
0.1%
Other values (5542)5690
95.3%
ValueCountFrequency (%)
0215
3.6%
0.421
 
< 0.1%
0.551
 
< 0.1%
0.651
 
< 0.1%
0.791
 
< 0.1%
0.843
 
0.1%
0.853
 
0.1%
1.071
 
< 0.1%
1.11
 
< 0.1%
1.258
 
0.1%
ValueCountFrequency (%)
280206.021
< 0.1%
259657.31
< 0.1%
194550.791
< 0.1%
168472.51
< 0.1%
143825.061
< 0.1%
124914.531
< 0.1%
117379.631
< 0.1%
91062.381
< 0.1%
81024.841
< 0.1%
77183.61
< 0.1%

devolutions
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct1325
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean150.19206
Minimum0
Maximum168469.6
Zeros4201
Zeros (%)70.4%
Negative0
Negative (%)0.0%
Memory size93.3 KiB
2021-06-06T15:16:03.209416image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q39.5
95-th percentile219.265
Maximum168469.6
Range168469.6
Interquartile range (IQR)9.5

Descriptive statistics

Standard deviation2602.83106
Coefficient of variation (CV)17.33001771
Kurtosis3072.075434
Mean150.19206
Median Absolute Deviation (MAD)0
Skewness50.91642437
Sum896796.79
Variance6774729.525
MonotonicityNot monotonic
2021-06-06T15:16:03.349869image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04201
70.4%
12.7522
 
0.4%
4.9519
 
0.3%
1517
 
0.3%
9.9515
 
0.3%
5.913
 
0.2%
25.511
 
0.2%
4.2510
 
0.2%
3.759
 
0.2%
19.99
 
0.2%
Other values (1315)1645
 
27.5%
ValueCountFrequency (%)
04201
70.4%
0.422
 
< 0.1%
0.651
 
< 0.1%
0.771
 
< 0.1%
0.951
 
< 0.1%
11
 
< 0.1%
1.256
 
0.1%
1.454
 
0.1%
1.641
 
< 0.1%
1.655
 
0.1%
ValueCountFrequency (%)
168469.61
< 0.1%
77183.61
< 0.1%
392671
< 0.1%
30032.231
< 0.1%
22998.41
< 0.1%
17836.461
< 0.1%
16888.021
< 0.1%
16453.711
< 0.1%
13541.332
< 0.1%
13474.791
< 0.1%

recency_p
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct304
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.0015073
Minimum0
Maximum373
Zeros38
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size93.3 KiB
2021-06-06T15:16:03.499318image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q124
median77
Q3215
95-th percentile365
Maximum373
Range373
Interquartile range (IQR)191

Descriptive statistics

Standard deviation118.7308916
Coefficient of variation (CV)0.9422973912
Kurtosis-0.8103832322
Mean126.0015073
Median Absolute Deviation (MAD)67
Skewness0.7488472968
Sum752355
Variance14097.02462
MonotonicityNot monotonic
2021-06-06T15:16:03.639659image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
365234
 
3.9%
1110
 
1.8%
4105
 
1.8%
399
 
1.7%
292
 
1.5%
1086
 
1.4%
882
 
1.4%
980
 
1.3%
1779
 
1.3%
778
 
1.3%
Other values (294)4926
82.5%
ValueCountFrequency (%)
038
 
0.6%
1110
1.8%
292
1.5%
399
1.7%
4105
1.8%
552
0.9%
778
1.3%
882
1.4%
980
1.3%
1086
1.4%
ValueCountFrequency (%)
37323
 
0.4%
37223
 
0.4%
37117
 
0.3%
3694
 
0.1%
36813
 
0.2%
36718
 
0.3%
36615
 
0.3%
365234
3.9%
36411
 
0.2%
3627
 
0.1%

recency_d
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct281
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean297.9606431
Minimum0
Maximum373
Zeros5
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size93.3 KiB
2021-06-06T15:16:03.782569image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile24
Q1282
median365
Q3365
95-th percentile365
Maximum373
Range373
Interquartile range (IQR)83

Descriptive statistics

Standard deviation120.0996751
Coefficient of variation (CV)0.4030722777
Kurtosis0.4613608502
Mean297.9606431
Median Absolute Deviation (MAD)0
Skewness-1.463353946
Sum1779123
Variance14423.93195
MonotonicityNot monotonic
2021-06-06T15:16:03.946035image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3654215
70.6%
845
 
0.8%
6439
 
0.7%
4631
 
0.5%
2131
 
0.5%
3528
 
0.5%
328
 
0.5%
927
 
0.5%
2523
 
0.4%
2922
 
0.4%
Other values (271)1482
 
24.8%
ValueCountFrequency (%)
05
 
0.1%
120
0.3%
213
 
0.2%
328
0.5%
415
 
0.3%
54
 
0.1%
710
 
0.2%
845
0.8%
927
0.5%
108
 
0.1%
ValueCountFrequency (%)
3731
 
< 0.1%
3728
 
0.1%
3712
 
< 0.1%
3692
 
< 0.1%
3689
 
0.2%
36711
 
0.2%
3667
 
0.1%
3654215
70.6%
3642
 
< 0.1%
3622
 
< 0.1%

quantity_p
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct817
Distinct (%)13.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean254.9107352
Minimum0
Maximum80996
Zeros215
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size93.3 KiB
2021-06-06T15:16:04.095750image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q134
median91
Q3200
95-th percentile631.5
Maximum80996
Range80996
Interquartile range (IQR)166

Descriptive statistics

Standard deviation1701.491022
Coefficient of variation (CV)6.674850396
Kurtosis1502.456269
Mean254.9107352
Median Absolute Deviation (MAD)70
Skewness35.09784254
Sum1522072
Variance2895071.697
MonotonicityNot monotonic
2021-06-06T15:16:04.239912image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1295
 
4.9%
0215
 
3.6%
3117
 
2.0%
653
 
0.9%
2850
 
0.8%
2148
 
0.8%
1648
 
0.8%
6742
 
0.7%
5241
 
0.7%
3641
 
0.7%
Other values (807)5021
84.1%
ValueCountFrequency (%)
0215
3.6%
1295
4.9%
226
 
0.4%
3117
 
2.0%
426
 
0.4%
521
 
0.4%
653
 
0.9%
725
 
0.4%
819
 
0.3%
917
 
0.3%
ValueCountFrequency (%)
809961
< 0.1%
742151
< 0.1%
386391
< 0.1%
213521
< 0.1%
173761
< 0.1%
171501
< 0.1%
162881
< 0.1%
158531
< 0.1%
133691
< 0.1%
128721
< 0.1%

quantity_d
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct189
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.92095126
Minimum-0
Maximum80995
Zeros4201
Zeros (%)70.4%
Negative0
Negative (%)0.0%
Memory size93.3 KiB
2021-06-06T15:16:04.395772image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-0
5-th percentile-0
Q1-0
median-0
Q31
95-th percentile28
Maximum80995
Range80995
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1435.845873
Coefficient of variation (CV)35.08828189
Kurtosis2885.966697
Mean40.92095126
Median Absolute Deviation (MAD)0
Skewness53.23013972
Sum244339
Variance2061653.371
MonotonicityNot monotonic
2021-06-06T15:16:04.538781image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-04201
70.4%
1512
 
8.6%
3174
 
2.9%
292
 
1.5%
690
 
1.5%
477
 
1.3%
546
 
0.8%
1246
 
0.8%
742
 
0.7%
840
 
0.7%
Other values (179)651
 
10.9%
ValueCountFrequency (%)
-04201
70.4%
1512
 
8.6%
292
 
1.5%
3174
 
2.9%
477
 
1.3%
546
 
0.8%
690
 
1.5%
742
 
0.7%
840
 
0.7%
937
 
0.6%
ValueCountFrequency (%)
809951
< 0.1%
742151
< 0.1%
93611
< 0.1%
90141
< 0.1%
48731
< 0.1%
40271
< 0.1%
23991
< 0.1%
23021
< 0.1%
21601
< 0.1%
16851
< 0.1%

invoices_p
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct60
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.339976553
Minimum0
Maximum209
Zeros215
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size93.3 KiB
2021-06-06T15:16:04.691438image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q34
95-th percentile11
Maximum209
Range209
Interquartile range (IQR)3

Descriptive statistics

Standard deviation6.736955851
Coefficient of variation (CV)2.01706681
Kurtosis316.395646
Mean3.339976553
Median Absolute Deviation (MAD)1
Skewness13.45819778
Sum19943
Variance45.38657414
MonotonicityNot monotonic
2021-06-06T15:16:04.847647image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12916
48.8%
2831
 
13.9%
3508
 
8.5%
4387
 
6.5%
5242
 
4.1%
0215
 
3.6%
6172
 
2.9%
7143
 
2.4%
898
 
1.6%
968
 
1.1%
Other values (50)391
 
6.5%
ValueCountFrequency (%)
0215
 
3.6%
12916
48.8%
2831
 
13.9%
3508
 
8.5%
4387
 
6.5%
5242
 
4.1%
6172
 
2.9%
7143
 
2.4%
898
 
1.6%
968
 
1.1%
ValueCountFrequency (%)
2091
< 0.1%
2011
< 0.1%
1241
< 0.1%
971
< 0.1%
931
< 0.1%
911
< 0.1%
861
< 0.1%
731
< 0.1%
631
< 0.1%
621
< 0.1%

invoices_d
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct27
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6421035003
Minimum0
Maximum47
Zeros4201
Zeros (%)70.4%
Negative0
Negative (%)0.0%
Memory size93.3 KiB
2021-06-06T15:16:04.967816image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum47
Range47
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.861996514
Coefficient of variation (CV)2.899838598
Kurtosis174.8938214
Mean0.6421035003
Median Absolute Deviation (MAD)0
Skewness10.06208452
Sum3834
Variance3.467031018
MonotonicityNot monotonic
2021-06-06T15:16:05.098273image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
04201
70.4%
11068
 
17.9%
2308
 
5.2%
3147
 
2.5%
497
 
1.6%
544
 
0.7%
630
 
0.5%
722
 
0.4%
89
 
0.2%
97
 
0.1%
Other values (17)38
 
0.6%
ValueCountFrequency (%)
04201
70.4%
11068
 
17.9%
2308
 
5.2%
3147
 
2.5%
497
 
1.6%
544
 
0.7%
630
 
0.5%
722
 
0.4%
89
 
0.2%
97
 
0.1%
ValueCountFrequency (%)
471
< 0.1%
451
< 0.1%
351
< 0.1%
311
< 0.1%
271
< 0.1%
231
< 0.1%
211
< 0.1%
192
< 0.1%
181
< 0.1%
172
< 0.1%

avg_ticket
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct5576
Distinct (%)93.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.59449396
Minimum0
Maximum77183.6
Zeros215
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size93.3 KiB
2021-06-06T15:16:05.232371image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.918888889
Q17.920322992
median15.7246
Q322.25928571
95-th percentile79.2140625
Maximum77183.6
Range77183.6
Interquartile range (IQR)14.33896272

Descriptive statistics

Standard deviation1274.292949
Coefficient of variation (CV)21.02984719
Kurtosis2881.449309
Mean60.59449396
Median Absolute Deviation (MAD)7.4314
Skewness51.96108487
Sum361809.7234
Variance1623822.519
MonotonicityNot monotonic
2021-06-06T15:16:05.377759image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0215
 
3.6%
3.7511
 
0.2%
4.9510
 
0.2%
2.959
 
0.2%
1.259
 
0.2%
7.958
 
0.1%
12.757
 
0.1%
8.257
 
0.1%
1.657
 
0.1%
5.956
 
0.1%
Other values (5566)5682
95.2%
ValueCountFrequency (%)
0215
3.6%
0.422
 
< 0.1%
0.5351
 
< 0.1%
0.551
 
< 0.1%
0.651
 
< 0.1%
0.791
 
< 0.1%
0.83714285711
 
< 0.1%
0.842
 
< 0.1%
0.853
 
0.1%
1.0022222221
 
< 0.1%
ValueCountFrequency (%)
77183.61
< 0.1%
56157.51
< 0.1%
13541.331
< 0.1%
13305.51
< 0.1%
11062.061
< 0.1%
4453.431
< 0.1%
4287.631
< 0.1%
38611
< 0.1%
30961
< 0.1%
2653.951
< 0.1%

avg_recency_days
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1280
Distinct (%)21.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120.87658
Minimum0
Maximum373
Zeros4
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size93.3 KiB
2021-06-06T15:16:05.528378image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14
Q139.63333333
median80
Q3182.5
95-th percentile338
Maximum373
Range373
Interquartile range (IQR)142.8666667

Descriptive statistics

Standard deviation102.9113998
Coefficient of variation (CV)0.8513758398
Kurtosis-0.2161395638
Mean120.87658
Median Absolute Deviation (MAD)53.3
Skewness0.9688375366
Sum721754.0593
Variance10590.75621
MonotonicityNot monotonic
2021-06-06T15:16:05.679123image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4637
 
0.6%
5334
 
0.6%
3933
 
0.6%
2832
 
0.5%
6032
 
0.5%
35331
 
0.5%
21329
 
0.5%
36728
 
0.5%
10628
 
0.5%
18428
 
0.5%
Other values (1270)5659
94.8%
ValueCountFrequency (%)
04
 
0.1%
111
0.2%
27
0.1%
2.5547945211
 
< 0.1%
314
0.2%
3.2434782611
 
< 0.1%
3.3008849561
 
< 0.1%
3.3333333331
 
< 0.1%
3.51
 
< 0.1%
3.6666666671
 
< 0.1%
ValueCountFrequency (%)
37321
0.4%
37222
0.4%
37118
0.3%
3694
 
0.1%
36814
0.2%
36728
0.5%
36613
0.2%
36519
0.3%
36411
 
0.2%
3627
 
0.1%

avg_basket_size
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct2372
Distinct (%)39.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean254.1652064
Minimum0
Maximum74215
Zeros215
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size93.3 KiB
2021-06-06T15:16:05.825244image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q164
median142.6666667
Q3282
95-th percentile718
Maximum74215
Range74215
Interquartile range (IQR)218

Descriptive statistics

Standard deviation1170.070864
Coefficient of variation (CV)4.603583947
Kurtosis2915.667275
Mean254.1652064
Median Absolute Deviation (MAD)98.66666667
Skewness49.85733829
Sum1517620.447
Variance1369065.827
MonotonicityNot monotonic
2021-06-06T15:16:05.968402image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0215
 
3.6%
1168
 
2.8%
272
 
1.2%
354
 
0.9%
451
 
0.9%
536
 
0.6%
628
 
0.5%
1226
 
0.4%
7321
 
0.4%
10021
 
0.4%
Other values (2362)5279
88.4%
ValueCountFrequency (%)
0215
3.6%
1168
2.8%
1.51
 
< 0.1%
272
 
1.2%
354
 
0.9%
3.3333333331
 
< 0.1%
451
 
0.9%
536
 
0.6%
5.3333333331
 
< 0.1%
5.6666666671
 
< 0.1%
ValueCountFrequency (%)
742151
< 0.1%
40498.51
< 0.1%
141491
< 0.1%
139561
< 0.1%
78241
< 0.1%
6009.3333331
< 0.1%
59641
< 0.1%
51981
< 0.1%
43001
< 0.1%
42801
< 0.1%

avg_variety
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1279
Distinct (%)21.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.20798644
Minimum0
Maximum1114
Zeros215
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size93.3 KiB
2021-06-06T15:16:06.121359image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q17.5
median17
Q334
95-th percentile171
Maximum1114
Range1114
Interquartile range (IQR)26.5

Descriptive statistics

Standard deviation75.8309033
Coefficient of variation (CV)1.984687244
Kurtosis33.55299436
Mean38.20798644
Median Absolute Deviation (MAD)11.75
Skewness5.086636807
Sum228139.887
Variance5750.325895
MonotonicityNot monotonic
2021-06-06T15:16:06.261288image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1331
 
5.5%
0215
 
3.6%
2164
 
2.7%
3114
 
1.9%
13102
 
1.7%
1097
 
1.6%
1496
 
1.6%
496
 
1.6%
595
 
1.6%
994
 
1.6%
Other values (1269)4567
76.5%
ValueCountFrequency (%)
0215
3.6%
1331
5.5%
1.21
 
< 0.1%
1.251
 
< 0.1%
1.3333333332
 
< 0.1%
1.59
 
0.2%
1.5555555561
 
< 0.1%
1.5714285711
 
< 0.1%
1.6666666674
 
0.1%
1.8333333331
 
< 0.1%
ValueCountFrequency (%)
11141
< 0.1%
7491
< 0.1%
7311
< 0.1%
7211
< 0.1%
7051
< 0.1%
6871
< 0.1%
6761
< 0.1%
6751
< 0.1%
6621
< 0.1%
6511
< 0.1%

purchases_pday
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct1243
Distinct (%)20.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5312110211
Minimum0
Maximum17
Zeros215
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size93.3 KiB
2021-06-06T15:16:06.405941image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.007782218992
Q10.02285714286
median0.6666666667
Q31
95-th percentile1
Maximum17
Range17
Interquartile range (IQR)0.9771428571

Descriptive statistics

Standard deviation0.5507143751
Coefficient of variation (CV)1.03671489
Kurtosis132.9126315
Mean0.5312110211
Median Absolute Deviation (MAD)0.5
Skewness4.713845887
Sum3171.861007
Variance0.3032863229
MonotonicityNot monotonic
2021-06-06T15:16:06.893958image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12925
49.0%
0215
 
3.6%
250
 
0.8%
0.062518
 
0.3%
0.0277777777817
 
0.3%
0.0238095238117
 
0.3%
0.0909090909115
 
0.3%
0.0833333333314
 
0.2%
0.0294117647113
 
0.2%
0.0769230769213
 
0.2%
Other values (1233)2674
44.8%
ValueCountFrequency (%)
0215
3.6%
0.0054495912811
 
< 0.1%
0.0054644808741
 
< 0.1%
0.0054794520551
 
< 0.1%
0.0054945054951
 
< 0.1%
0.0055865921792
 
< 0.1%
0.0056022408961
 
< 0.1%
0.0056179775282
 
< 0.1%
0.005665722381
 
< 0.1%
0.0056818181822
 
< 0.1%
ValueCountFrequency (%)
171
 
< 0.1%
42
 
< 0.1%
34
 
0.1%
250
 
0.8%
1.1428571431
 
< 0.1%
12925
49.0%
0.751
 
< 0.1%
0.66666666674
 
0.1%
0.55882352941
 
< 0.1%
0.53887399461
 
< 0.1%

Interactions

2021-06-06T15:15:38.698949image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:38.842013image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:38.961169image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:39.086156image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:39.212258image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:39.336193image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:39.463392image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:39.586682image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:39.701035image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:39.817518image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:39.951250image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:40.080806image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:40.203634image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:40.320270image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:40.444673image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:40.563915image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:40.675538image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:40.796822image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:40.919132image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:41.041660image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:41.167780image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:41.307796image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:41.439465image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:41.562733image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:41.689834image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:41.806540image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:41.925995image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:42.042597image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:42.166051image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:42.296358image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:42.570873image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:42.695024image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:42.809210image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:42.921637image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:43.037329image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:43.153893image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:43.261170image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:43.368093image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:43.484731image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:43.601180image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:43.721378image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:43.830428image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:43.948472image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:44.063178image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:44.171267image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:44.288032image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:44.401331image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:44.512500image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:44.631129image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:44.746807image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:44.851620image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:44.956423image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:45.079666image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:45.203119image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:45.328769image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:45.447158image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:45.572101image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:45.693099image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:45.813288image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:45.929935image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:46.041298image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:46.152874image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:46.269808image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:46.387063image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:46.492729image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:46.597590image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:46.713441image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:46.825310image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:47.112619image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:47.232572image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:47.347237image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:47.462437image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:47.575538image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:47.693471image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:47.810477image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:47.928329image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:48.050551image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:48.170920image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:48.280895image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:48.398589image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:48.522798image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:48.641038image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:48.761085image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:48.873346image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:48.991927image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:49.112460image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:49.239121image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:49.362204image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:49.478390image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:49.594032image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:49.713816image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:49.838221image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:49.948082image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:50.059180image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:50.178731image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:50.296899image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:50.414669image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:50.526488image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:50.644063image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:50.743026image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:50.841596image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:50.944622image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:51.046143image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:51.148281image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:51.253298image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:51.358997image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:51.453478image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:51.550508image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:51.656002image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:51.757813image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:51.860968image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:51.958901image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:52.067289image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:52.165310image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:52.264571image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:52.370383image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:52.687819image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:52.801958image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:52.909046image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:53.015124image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:53.111446image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:53.209139image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:53.321598image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:53.428893image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:53.532155image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:53.630024image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:53.733940image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:53.847648image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:53.960156image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:54.077348image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:54.192671image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:54.308348image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:54.429135image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:54.548479image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:54.657259image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:54.772169image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:54.900521image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:55.017472image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:55.133934image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:55.245929image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:55.363053image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:55.473844image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:55.583014image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:55.696610image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:55.808688image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:55.923218image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:56.044312image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:56.162087image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:56.267140image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:56.372120image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:56.487038image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:56.599723image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:56.713298image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:56.821433image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:56.935437image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:57.045967image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:57.156616image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:57.270320image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:57.383060image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:57.495355image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:57.611271image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:57.728441image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:57.835099image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:57.940615image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:58.056496image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:58.169718image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:58.283843image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:58.403436image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:58.520461image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:58.621868image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:58.725747image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:58.831996image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:58.936779image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:59.041687image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:59.413753image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:59.535513image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:59.660995image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:59.783998image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:15:59.896006image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:16:00.001867image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:16:00.109765image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:16:00.210937image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:16:00.322628image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:16:00.434362image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:16:00.543228image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:16:00.658663image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:16:00.773479image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:16:00.887751image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:16:01.004252image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:16:01.117984image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:16:01.223010image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:16:01.327534image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:16:01.443927image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:16:01.557708image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:16:01.669950image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-06T15:16:01.792033image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2021-06-06T15:16:07.042058image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-06-06T15:16:07.235500image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-06-06T15:16:07.436641image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-06-06T15:16:07.625777image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-06-06T15:16:02.035547image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-06-06T15:16:02.393498image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

customer_idpurchasesdevolutionsrecency_precency_dquantity_pquantity_dinvoices_pinvoices_davg_ticketavg_recency_daysavg_basket_sizeavg_varietypurchases_pday
0178505391.21102.58372.0302.035.021.034.01.018.152222124.33333350.9705888.73529417.000000
1130473237.54158.4431.031.0132.06.010.08.018.82290726.642857139.10000017.2000000.029155
2125837281.3894.042.056.01569.050.015.03.029.47927120.722222337.33333316.4666670.040323
313748948.250.0095.0365.0169.0-0.05.00.033.86607193.25000087.8000005.6000000.017921
415100876.00240.90333.0330.048.022.03.03.0292.00000062.16666726.6666671.0000000.073171
5152914668.3071.7925.0172.0508.027.015.05.045.32330121.941176140.2000006.8666670.042980
6146885630.87523.497.07.0579.0281.021.06.017.21978617.761905172.42857115.5714290.057221
7178095411.91784.2916.016.0961.041.012.03.088.71983631.083333171.4166675.0833330.033520
81531160767.901348.560.00.02167.0231.091.027.025.5434644.098901419.71428626.1428570.243316
9145278508.82797.442.08.0198.03.055.031.08.7539305.82812537.98181817.6727270.149457

Last rows

customer_idpurchasesdevolutionsrecency_precency_dquantity_pquantity_dinvoices_pinvoices_davg_ticketavg_recency_daysavg_basket_sizeavg_varietypurchases_pday
5961227004839.420.01.0365.0917.0-0.01.00.078.0551611.01074.062.01.0
596213298360.000.01.0365.096.0-0.01.00.0180.0000001.096.02.01.0
596314569227.390.01.0365.070.0-0.01.00.018.9491671.079.012.01.0
59642270417.900.01.0365.02.0-0.01.00.02.5571431.014.07.01.0
5965227053.350.01.0365.01.0-0.01.00.01.6750001.02.02.01.0
5966227066637.590.01.0365.0430.0-0.01.00.010.4528981.01748.0635.01.0
5967227077689.230.00.0365.0347.0-0.01.00.010.5187820.02011.0731.01.0
5968227083217.200.00.0365.0524.0-0.01.00.054.5288140.0654.059.01.0
5969227095664.890.00.0365.0211.0-0.01.00.025.9857340.0732.0218.01.0
597012713848.550.00.0365.0101.0-0.01.00.022.3302630.0508.038.01.0